In various types of computer systems, there may be a need to collect, maintain, and utilize confidential data. In some instances, users may be reluctant to share this confidential data over privacy concerns. These concerns extend not only to pure security concerns, such as concerns over whether third parties such as hackers may gain access to the confidential data, but also to how the computer system itself may utilize the confidential data. With certain types of data, users providing the data may be somewhat comfortable with uses of the data that maintain anonymity, such as the confidential data merely being used to provide broad statistical analysis to other users.
One example of such confidential data is salary/compensation information. It may be desirable for a service such as a social networking service to entice its members to provide information about their salary or other work-related compensation in order to provide members with insights as to various metrics regarding salary/compensation, such as an average salary for a particular job type in a particular city. There are technical challenges encountered, however, in ensuring that such confidential data remains confidential and is only used for specific purposes, and it can be difficult to persuade members to provide such confidential data due to their concerns that these technical challenges may not be met. Once the confidential data is acquired, it can be utilized for a variety of different statistical insights that can be presented to users. It would be desirable, however, to also utilize the confidential data to make various recommendations to the user, but such recommendations present technical challenges in that it is difficult for a computer to predict along a particular dimension while simultaneously correcting for the confidential data.